.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_plot_train_classifier.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_plot_train_classifier.py:


============================
Train an Acoustic Classifier
============================

Train an acoustic classifier on speech or noise features.

To see how soundpy implements this, see `soundpy.models.builtin.envclassifier_train`.


.. code-block:: default

    import os, sys
    import inspect
    currentdir = os.path.dirname(os.path.abspath(
        inspect.getfile(inspect.currentframe())))
    parentdir = os.path.dirname(currentdir)
    parparentdir = os.path.dirname(parentdir)
    packagedir = os.path.dirname(parparentdir)
    sys.path.insert(0, packagedir)

    import matplotlib.pyplot as plt
    import IPython.display as ipd
    package_dir = '../../../'
    os.chdir(package_dir)
    sp_dir = package_dir









Let's import soundpy for handling sound


.. code-block:: default

    import soundpy as sp







As well as the deep learning component of soundpy


.. code-block:: default

    from soundpy import models as spdl









Prepare for Training: Data Organization
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Set path relevant for audio data for this example

I will load previously extracted features (from the Speech Commands Dataset) 
See `soundpy.feats.save_features_datasets` or `soundpy.builtin.envclassifier_feats`


.. code-block:: default

    feature_extraction_dir = '{}audiodata2/example_feats_models/'.format(sp_dir)+\
        'envclassifier/example_feats_fbank/'








What is in this folder?


.. code-block:: default

    feature_extraction_dir = sp.utils.check_dir(feature_extraction_dir)
    files = list(feature_extraction_dir.glob('*.*'))
    for f in files:
        print(f.name)
  




.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    dict_encode.csv
    dataset_audio_assignments.csv
    test_data_fbank.npy
    log_extraction_settings.csv
    dataset_audiofiles.csv
    train_data_fbank.npy
    val_data_fbank.npy
    dict_decode.csv
    dict_encdodedlabel2audio.csv




The .npy files contain the features themselves, in train, validation, and
test datasets:


.. code-block:: default

    files = list(feature_extraction_dir.glob('*.npy'))
    for f in files:
        print(f.name)
  




.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    test_data_fbank.npy
    train_data_fbank.npy
    val_data_fbank.npy




The .csv files contain information about how the features were extracted


.. code-block:: default

    files = list(feature_extraction_dir.glob('*.csv'))
    for f in files:
        print(f.name)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    dict_encode.csv
    dataset_audio_assignments.csv
    log_extraction_settings.csv
    dataset_audiofiles.csv
    dict_decode.csv
    dict_encdodedlabel2audio.csv




We'll have a look at which features were extracted and other settings:


.. code-block:: default

    feat_settings = sp.utils.load_dict(
        feature_extraction_dir.joinpath('log_extraction_settings.csv'))
    for key, value in feat_settings.items():
        print(key, ' --> ', value)
    




.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    dur_sec  -->  1
    feature_type  -->  fbank
    feat_type  -->  fbank
    complex_vals  -->  False
    sr  -->  22050
    num_feats  -->  40
    n_fft  -->  441
    win_size_ms  -->  20
    frame_length  -->  441
    percent_overlap  -->  0.5
    frames_per_sample  -->  
    labeled_data  -->  True
    visualize  -->  True
    input_shape  -->  (101, 41)
    desired_shape  -->  (101, 40)
    use_librosa  -->  True
    center  -->  True
    mode  -->  reflect
    subsection_data  -->  False
    divide_factor  -->  5
    kwargs  -->  {'sr': 22050, 'feature_type': 'fbank', 'win_size_ms': 20, 'percent_overlap': 0.5, 'num_filters': 40, 'num_mfcc': 40, 'dur_sec': 1, 'center': True}




For more about these settings, see `soundpy.feats.save_features_datasets`.

We'll have a look at the audio files that were assigned 
to the train, val, and test datasets. 


.. code-block:: default

    audio_datasets = sp.utils.load_dict(
        feature_extraction_dir.joinpath('dataset_audiofiles.csv'))
    count = 0
    for key, value in audio_datasets.items():
        print(key, ' --> ', value)
        count += 1
        if count > 5:
            break





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Dictionary values or size is too large.
    Maxing out field size limit for loading this dictionary:
    ../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/dataset_audiofiles.csv

    The new field size limit is:
    9223372036854775807
    train  -->  [(0, '/home/airos/Projects/Data/sound/speech_commands_small_section/bed/19e246ad_nohash_0.wav'), (2, '/home/airos/Projects/Data/sound/speech_commands_small_section/dog/e102119e_nohash_0.wav'), (0, '/home/airos/Projects/Data/sound/speech_commands_small_section/bed/4d9e07cf_nohash_0.wav'), (2, '/home/airos/Projects/Data/sound/speech_commands_small_section/dog/8335f893_nohash_1.wav'), (0, '/home/airos/Projects/Data/sound/speech_commands_small_section/bed/541120c7_nohash_0.wav'), (1, '/home/airos/Projects/Data/sound/speech_commands_small_section/cat/8d4cdc60_nohash_0.wav'), (4, '/home/airos/Projects/Data/sound/speech_commands_small_section/tree/73af0c50_nohash_0.wav'), (1, '/home/airos/Projects/Data/sound/speech_commands_small_section/cat/8056e897_nohash_1.wav'), (3, '/home/airos/Projects/Data/sound/speech_commands_small_section/five/acde7e73_nohash_1.wav'), (3, '/home/airos/Projects/Data/sound/speech_commands_small_section/five/5c8af87a_nohash_4.wav'), (3, '/home/airos/Projects/Data/sound/speech_commands_small_section/five/1626bc5a_nohash_0.wav'), (2, '/home/airos/Projects/Data/sound/speech_commands_small_section/dog/cf87b736_nohash_0.wav'), (0, '/home/airos/Projects/Data/sound/speech_commands_small_section/bed/dcc012ec_nohash_0.wav'), (3, '/home/airos/Projects/Data/sound/speech_commands_small_section/five/bf70feda_nohash_1.wav'), (1, '/home/airos/Projects/Data/sound/speech_commands_small_section/cat/a42a88ff_nohash_1.wav'), (1, '/home/airos/Projects/Data/sound/speech_commands_small_section/cat/becd5a53_nohash_0.wav'), (4, '/home/airos/Projects/Data/sound/speech_commands_small_section/tree/fb9d6d23_nohash_2.wav'), (3, '/home/airos/Projects/Data/sound/speech_commands_small_section/five/4d4e17f5_nohash_0.wav'), (3, '/home/airos/Projects/Data/sound/speech_commands_small_section/five/4407ba92_nohash_0.wav'), (1, '/home/airos/Projects/Data/sound/speech_commands_small_section/cat/c137814b_nohash_0.wav'), (4, 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Built-In Functionality: soundpy does everything for you
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
For more about this function, see `soundpy.models.builtin.envclassifier_train`.


.. code-block:: default

    model_dir, history = spdl.envclassifier_train(
        feature_extraction_dir = feature_extraction_dir,
        epochs = 10,
        patience = 5)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none



    The model will be trained 10 epochs per training session. 
    Total possible epochs: 10



    TRAINING SESSION  1
    Training on: 
    ../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/train_data_fbank.npy

    <FlatMapDataset shapes: ((1, 101, 40, 1), (1,)), types: (tf.float64, tf.float64)>
    <FlatMapDataset shapes: ((1, 101, 40, 1), (1,)), types: (tf.float64, tf.float64)>
    <FlatMapDataset shapes: ((1, 101, 40, 1), (1,)), types: (tf.float64, tf.float64)>
    Epoch 1/10

       1/7433 [..............................] - ETA: 0s - loss: 1.6111 - accuracy: 0.0000e+00
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    Epoch 00001: val_loss improved from inf to 0.40003, saving model to ../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/model_cnn_classifier_9m3d13h26m22s320ms/model_cnn_classifier_9m3d13h26m22s320ms.h5
    
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    Epoch 2/10

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    Epoch 00002: val_loss improved from 0.40003 to 0.35578, saving model to ../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/model_cnn_classifier_9m3d13h26m22s320ms/model_cnn_classifier_9m3d13h26m22s320ms.h5
    
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    Epoch 3/10

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    Epoch 00003: val_loss improved from 0.35578 to 0.35275, saving model to ../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/model_cnn_classifier_9m3d13h26m22s320ms/model_cnn_classifier_9m3d13h26m22s320ms.h5
    
    7433/7433 [==============================] - 13s 2ms/step - loss: 0.3585 - accuracy: 0.8733 - val_loss: 0.3527 - val_accuracy: 0.8963
    Epoch 4/10

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    Epoch 00004: val_loss improved from 0.35275 to 0.35026, saving model to ../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/model_cnn_classifier_9m3d13h26m22s320ms/model_cnn_classifier_9m3d13h26m22s320ms.h5
    
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    Epoch 5/10

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    Epoch 00005: val_loss did not improve from 0.35026
    
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    Epoch 6/10

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    Epoch 00006: val_loss did not improve from 0.35026
    
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    Epoch 7/10

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    Epoch 00007: val_loss improved from 0.35026 to 0.30664, saving model to ../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/model_cnn_classifier_9m3d13h26m22s320ms/model_cnn_classifier_9m3d13h26m22s320ms.h5
    
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    Epoch 8/10

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    Epoch 00008: val_loss did not improve from 0.30664
    
    7433/7433 [==============================] - 13s 2ms/step - loss: 0.2391 - accuracy: 0.9177 - val_loss: 0.3397 - val_accuracy: 0.8898
    Epoch 9/10

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    Epoch 00009: val_loss did not improve from 0.30664
    
    7433/7433 [==============================] - 13s 2ms/step - loss: 0.2173 - accuracy: 0.9251 - val_loss: 0.3421 - val_accuracy: 0.9104
    Epoch 10/10

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    7048/7433 [===========================>..] - ETA: 0s - loss: 0.2109 - accuracy: 0.9268
    7074/7433 [===========================>..] - ETA: 0s - loss: 0.2106 - accuracy: 0.9268
    7100/7433 [===========================>..] - ETA: 0s - loss: 0.2101 - accuracy: 0.9269
    7126/7433 [===========================>..] - ETA: 0s - loss: 0.2095 - accuracy: 0.9270
    7152/7433 [===========================>..] - ETA: 0s - loss: 0.2091 - accuracy: 0.9269
    7178/7433 [===========================>..] - ETA: 0s - loss: 0.2087 - accuracy: 0.9270
    7204/7433 [============================>.] - ETA: 0s - loss: 0.2101 - accuracy: 0.9266
    7230/7433 [============================>.] - ETA: 0s - loss: 0.2102 - accuracy: 0.9266
    7255/7433 [============================>.] - ETA: 0s - loss: 0.2108 - accuracy: 0.9267
    7281/7433 [============================>.] - ETA: 0s - loss: 0.2104 - accuracy: 0.9268
    7307/7433 [============================>.] - ETA: 0s - loss: 0.2107 - accuracy: 0.9268
    7333/7433 [============================>.] - ETA: 0s - loss: 0.2113 - accuracy: 0.9266
    7358/7433 [============================>.] - ETA: 0s - loss: 0.2108 - accuracy: 0.9269
    7383/7433 [============================>.] - ETA: 0s - loss: 0.2109 - accuracy: 0.9269
    7408/7433 [============================>.] - ETA: 0s - loss: 0.2108 - accuracy: 0.9268
    7433/7433 [==============================] - ETA: 0s - loss: 0.2105 - accuracy: 0.9269
    Epoch 00010: val_loss did not improve from 0.30664
    
    7433/7433 [==============================] - 14s 2ms/step - loss: 0.2105 - accuracy: 0.9269 - val_loss: 0.4356 - val_accuracy: 0.8909

      1/500 [..............................] - ETA: 0s - loss: 0.0041 - accuracy: 1.0000
     53/500 [==>...........................] - ETA: 0s - loss: 0.7603 - accuracy: 0.7925
    112/500 [=====>........................] - ETA: 0s - loss: 0.5648 - accuracy: 0.8214
    169/500 [=========>....................] - ETA: 0s - loss: 0.4342 - accuracy: 0.8580
    224/500 [============>.................] - ETA: 0s - loss: 0.5400 - accuracy: 0.8527
    279/500 [===============>..............] - ETA: 0s - loss: 0.5559 - accuracy: 0.8638
    332/500 [==================>...........] - ETA: 0s - loss: 0.5506 - accuracy: 0.8584
    383/500 [=====================>........] - ETA: 0s - loss: 0.5340 - accuracy: 0.8668
    441/500 [=========================>....] - ETA: 0s - loss: 0.5098 - accuracy: 0.8730
    493/500 [============================>.] - ETA: 0s - loss: 0.5546 - accuracy: 0.8621
    500/500 [==============================] - 0s 919us/step - loss: 0.5479 - accuracy: 0.8640
    Test loss: 0.5479004979133606
    Test accuracy: 0.8640000224113464

    Finished training the model. The model and associated files can be found here: 
    ../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/model_cnn_classifier_9m3d13h26m22s320ms




Where the model and logs are located:


.. code-block:: default

    model_dir





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    PosixPath('../../../audiodata2/example_feats_models/envclassifier/example_feats_fbank/model_cnn_classifier_9m3d13h26m22s320ms')



Let's plot how the model performed (on this mini dataset)


.. code-block:: default

    import matplotlib.pyplot as plt
    plt.clf()
    plt.plot(history.history['accuracy'])
    plt.plot(history.history['val_accuracy'])
    plt.title('model accuracy')
    plt.ylabel('accuracy')
    plt.xlabel('epoch')
    plt.legend(['train', 'val'], loc='upper right')
    plt.savefig('accuracy.png')



.. image:: /auto_examples/images/sphx_glr_plot_train_classifier_001.png
    :alt: model accuracy
    :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 2 minutes  16.059 seconds)


.. _sphx_glr_download_auto_examples_plot_train_classifier.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_train_classifier.py <plot_train_classifier.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_train_classifier.ipynb <plot_train_classifier.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_